Project: Mapping and use of QTL for Marker-Assisted Improvement of Meat Quality in Pigs
Agency: USDA IFAFS
PI: Dekkers, J.
Co-PI: Rodriguez-Zas, Sandra.
Grant Number: 00-52100-9610
Link: http://www.reeusda.gov/ifafs/ifafssum2.htm
Abstract:
Forty-three percent of the red meat consumed in the world is pork, making it an exceedingly important product. There are key quality and processing characteristics that are important for the future profitability and competitiveness of the swine industry. Improving meat quality genetically is extremely difficult with standard selection methods, but would be much more tenable if the genes responsible for pork meat quality are mapped. This project will be the first extended large-scale study on identifying quantitative trait loci (QTL) for pork quality traits in commercial breeds. Obtaining QTL information in commercial breeds is crucial for implementation of marker-assisted selection in industry programs. This project will utilize resources that have already been established, to a large degree with financial support from the industry, in the form of two phenotyped and genotyped F2 resource populations from commercial breeds, one at Iowa State University and one at the University of Illinois. The goals of this project are: 1) complete phenotyping and genotyping the two resource populations; 2) develop and apply methods for combined analysis of data from the two resource populations to detect QTL that segregate between and within breeds; 3) develop strategies for the use of the identified QTL in industry breeding programs; and, 4) facilitate the use of results by the industry. The research and education program will ensure that all interested stakeholders will have access to the results of this study that will enhance industry implementation. Although this project focuses on meat quality in pigs, the statistical approaches developed will be useful for other livestock species.
Associated Publications:
Li X., S. L. Rodriguez-Zas, J. E. Beever, M. Ellis, F. McKeith and B. A. Bailey. 2002. Parametric and non-parametric repeated-measure analysis of growth patterns. In: Proceedings of the American Statistical Association Annual Meeting, New York, New York.
Li X., S. L. Rodriguez-Zas, J. E. Beever, M. Ellis, F. McKeith and B. A. Bailey. 2002. Mixed effects models to describe growth in related subjects. In Proceedings of the 2002 Bohrer workshop, Department of Statistics, University of Illinois at Urbana-Champaign.